A novel prediction-based strategy for object tracking in sensor networks by mining seamless temporal movement patterns

Lin, Kun‐Wei; Hsieh, Ming-Hua; Tseng, Vincent S.

doi:10.1016/j.eswa.2009.09.011

Cited by 27 publications

(11 citation statements)

References 25 publications

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“…Lin et al [26] improved Tseng and Lin's [36] proposed method and used it on WSNs. In this method, energy consumption was reduced by discovering seamless temporal movement patterns and considering the storage and energy constraints.…”

Section: Related Workmentioning

confidence: 99%

“…From [26,32,36], we know that data mining techniques applied to the prediction for target tracking in wireless sensor networks can obtain better prediction accuracy. In addition, distributed approaches for target tracking can lower power consumption.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, many studies [7,13,26,27,36,37] have explored how to mine interesting patterns from object movement data and using these patterns to predict an object's location. The advantage of data mining is that it can find interesting patterns from the database by data processing.…”

Section: Related Workmentioning

confidence: 99%

“…These algorithms reduce energy consumption by using a state transition model to transition the node's active state to a sleep state at a scheduled time, and causing them to remain in a sleep state for as long as possible. In addition to scheduling, other methods [8,25,26,32,36,37,41] use the movement and location information of an object to predict object future location, tracking the object by waking the predicted node(s) and transiting other nodes to a sleep state. The advantage of these methods is that a majority of nodes in the network are in a sleep state and only a few nodes are active, which reduces energy consumption and extends the lifetime of the network.…”

Section: Related Workmentioning

confidence: 99%

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Distributed object tracking using moving trajectories in wireless sensor networks

Chen

2015

Wireless Netw

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Most recent research on object tracking sensor networks has focused on collecting all data from the sensor network into the sink, which delivers the predicted locations to the corresponding nodes in order to accurately predict object movement. The communication cost of this centralized scenario is higher than that of a distributed method. Centralized data collection affects the freshness of the data and increases latency in movement trajectory prediction. In addition, due to the large amount of packets being sent and received, sensor node energy is quickly exhausted. Although this data collection method might result in higher accuracy for prediction, the sensor network lifetime is not reduced. In this paper, a distributed object tracking method is proposed using the network structure of convex polygons, called faces. The nodes in the faces cooperate to find the trajectories of an object and then these trajectories are used to predict the objects' movement. The proposed method, based on trajectory tree construction, can reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and make prediction of nodes moving direction accurately than the existing approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Distributed object tracking using moving trajectories in wireless sensor networks

Chen

2015

Wireless Netw

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“…• Intrusion Detection and Tracking: Detection, classification, and tracking of intruders/targets/objects are a basic surveillance or military application of WSN [11] and have been studied in the literature from many aspects [11][12][13][14][15][16][17][18]. Such applications concern how fast the WSN can detect certain intruders/targets/ objects and how reliably the sensing and detection data can be reported to the BS.…”

Section: Applications Of Wireless Sensor Networkmentioning

confidence: 99%

Coverage, Connectivity, and Deployment in Wireless Sensor Networks

Wang

Zhang

Liu

et al. 2014

Signals and Communication Technology

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Recent years have witnessed successful real-world deployments of wireless sensor networks (WSNs) in a wide range of civil and military applications. Sensing coverage and network connectivity are two of the most fundamental issues to ensure effective environmental sensing and robust data communication in a WSN application. This chapter presents fundamental studies on the sensing coverage and the network connectivity from mathematical modeling, theoretical analysis, and performance evaluation perspectives. Both lattice WSNs that follow a pattern-based deployment strategy and random WSNs that follow a random deployment strategy are considered. The aim of this chapter is to deliver a systematic study on the fundamental problems in WSNs and provide guidelines in selecting critical network parameters for WSN design and implementation in practice.

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